rasch_model

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It usefully discloses that eRm::RM is used and that arbitrary code is rejected, but it does not describe estimation behavior, missing-data handling, or any operational constraints beyond code rejection.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence with no filler. It clearly states the action, model type, implementation, and a critical constraint without wasting words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is narrow and an output schema exists, so some return-value details are handled elsewhere. Still, the description lacks usage context, parameter clarification, and deeper behavioral details, leaving the agent to infer several things needed for confident invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate, but it does not explain the 'data' parameter, 'responses', or 'item_names'. The word 'dichotomous' hints that responses should be binary, but the meaning and format of parameters are left entirely to the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific action ('Fit'), a specific resource ('fixed dichotomous Rasch model'), and the implementation ('eRm::RM'). This clearly differentiates it from sibling tools like ctt_item_analysis or correlation_matrix.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The intended use is implied by the phrase 'Fit a fixed dichotomous Rasch model', and the explicit rule 'arbitrary code is never accepted' guides input type. However, it does not state when to prefer this tool over alternatives or mention prerequisites such as suitability only for binary response data.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.